Spoken dialogue systems are intended to verbally interact with humans and are becoming increasingly prevalent, in particular in mobile applications and wearable speech interface devices.
Spoken dialogue systems generally comprise a number of components to, for example, convert a human user's speech into text, identify and collate semantic information, control the flow of the conversation in order to acquire the necessary information to complete the task, generate the necessary text and synthesize speech. Dialogue managers are often responsible for the state and flow of the conversation between the spoken dialogue system and the user.
A dialogue manager is traditionally tailored to work in a specific domain. The domain will generally be specific to the type and purpose of the spoken dialogue system, for example a system to provide the user with a number of restaurants which match certain criteria, or a system to identify a suitable laptop for a buyer.
The domain will often have a domain-specific ontology which comprises the types, properties and interrelationships of the entities specific to that domain. In many task-oriented spoken dialogue systems the ontology will specify a plurality of slots to be filled with one (or multiple) of a plurality of possible values. The dialogue manager will generally comprise a policy designed to control the flow of the conversation to fill slots with a value in order to complete the task.
In order to increase the efficiency and effectiveness of a spoken dialogue system, the policy will be optimised for the domain and ontology with which it operates. This is often done using data-driven policy adaptation processes, e.g. using the spoken dialogue system with a user or a simulated user and adapting the policy to increase at least one of the success rate or average reward over a value for an unadapted policy. This is a time consuming and expensive process. Moreover, in many cases a basic (e.g. rule-based) working SDS will be required before the data collection procedure can be started. Developing the initial system for a new domain requires a significant amount of human expertise.
As the ontology is generally domain-dependent, a policy optimised for a first domain is traditionally not compatible with, or not optimised for, a second domain. This means that a policy must be optimised for every domain independently. This can be an expensive and time consuming exercise and means an optimised dialogue manager cannot be provided for a domain without first spending a significant amount of time and money optimising it to the specific ontology of that domain.